Curtis Hamman

The rapidly rising demand for energy has stimulated the growth of clean, safe and renewable energy technologies. One such technology is nuclear power, which currently supplies one-fifth of the total electricity generated in the United States. Developing methods to increase the power output and longevity of existing reactors is a critical first step towards preserving national energy security and meeting worldwide demand. Advanced modeling and simulation can achieve this by providing improved predictive capabilities that enhance reactor design and operation.

The next generation of nuclear power plants are designed to operate at much higher temperatures in order to provide the energy needed to heat our homes and produce hydrogen fuel to power our automobiles. At such high temperatures, the coupled multiphysics response of natural convection and turbulence is poorly understood. As a result, current turbulence models fail to accurately predict the local and integral heat transfer characteristics of high Rayleigh number natural convection in internally-heated reactor vessels. This is a direct result of the severe anisotropy of turbulent transport that occurs due to unstable buoyancy-induced stratification. Understanding how coolant transports heat away from the core and how this is coupled to the core neutronics leads to more robust, efficient, and safe nuclear energy systems.

To improve our understanding of these phenomena, I am building a multiphysics simulation platform for nuclear energy applications. This platform enables the development of accurate turbulence models to form the foundation for nuclear energy simulation. By leveraging advanced computing tools developed through ASC at the Center for Turbulence Research, I am performing Large Eddy Simulations of practical reactor core geometries, such as fuel rod bundles, to develop physical models of coolant transport, heat transfer, and neutronics. With this predictive simulation science platform, next generation nuclear power plant designs will be explored and optimized.

Journal Publications

1. C.W. Hamman, J.C. Klewicki and R.M. Kirby. "On the Lamb vector divergence in Navier-Stokes flows." Journal of Fluid Mechanics, Vol. 610 p. 261-284, 2008.
2. C.W. Hamman, R.M. Kirby and M. Berzins. "Parallelization and scalability of a spectral element channel flow solver for incompressible Navier-Stokes equations." Concurrency and Computation: Practice & Experience, 19:10 p. 1403-1422, 2007.

1. C.W. Hamman and P. Moin, "Acoustic Detection of Boiling in Nuclear Reactors," Received Best Poster Award (1st Place), Stanford TFSA Conference, Feb. 2010.
2. C.W. Hamman and P. Moin, "Effects of Shear on Heat Transfer in Nuclear Reactors," Stanford TFSA Conference, February 2010.
3. C.W. Hamman and P. Moin, "Effects of Shear on Unstably Stratified Convection," 62nd Annual Meeting of the American Physical Society, Division of Fluid Dynamics, Minneapolis, MN, November 2009.
4. C.W. Hamman and P. Moin, "The Sound of Boiling," DOE CSGF Annual Fellow's Conference, July 2009.
5. C.W. Hamman, "Atomic Power for Petascale Computing", Received Best Poster Award (1st Place), Stanford High Performance Computing Conference, 2008.
6. C.W. Hamman, "Towards Predictive Simulation for Nuclear Energy Applications", Stanford High Performance Computing Conference, 2008.
7. C.W. Hamman and P. Moin, "Fundamental Research Needs for Nuclear Energy Simulation," Stanford TFSA Conference, February 2008.
8. C.W. Hamman, R.M. Kirby and J.C. Klewicki, "On the Lamb vector divergence as a momentum field diagnostic employed in turbulent channel flow," 59th Annual Meeting of the American Physical Society, Division of Fluid Dynamics, Tampa Bay, FL, November 2006.

1. C.W. Hamman, "Predictive Parallel Performance Models for Petascale Platforms", Stanford High Performance Computing Seminar Series, May 2009.